A method to analyze first-order spatial properties of optical flow is proposed. The approach is based on the use of a set of linear models that dynamically adjust their properties on the basis of context information. These models are generated by a recursive network that takes into account spatial interaction between neighbors. By checking the presence of these models in the optic flow using a multiple model Kalman Filter it is possible to recover information about the coefficients of the affine description and the image motion invariants: divergence, curl and deformation. Reliable estimates of these quantities could help in the analysis of real world complex motion sequences. Experimental results on egomotion estimation and 3D surface reconstruction validate the approach.
Motion interpretation using adjustable linear models
CHESSA, MANUELA;SOLARI, FABIO;SABATINI, SILVIO PAOLO;BISIO, GIACOMO
2008-01-01
Abstract
A method to analyze first-order spatial properties of optical flow is proposed. The approach is based on the use of a set of linear models that dynamically adjust their properties on the basis of context information. These models are generated by a recursive network that takes into account spatial interaction between neighbors. By checking the presence of these models in the optic flow using a multiple model Kalman Filter it is possible to recover information about the coefficients of the affine description and the image motion invariants: divergence, curl and deformation. Reliable estimates of these quantities could help in the analysis of real world complex motion sequences. Experimental results on egomotion estimation and 3D surface reconstruction validate the approach.
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